FORECASTING VALUE-AT-RISK WITH TWO-STEP METHOD: GARCH-EXPONENTIATED ODD LOG-LOGISTIC NORMAL MODEL

Emrah Altun (), Morad Alizadeh (), Gamze Ozel (), Hüseyin Tatlidil () and Najmieh Maksayi ()
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Emrah Altun: Department of Statistics, Hacettepe University, Turkey. Corresponding Author.
Morad Alizadeh: Department of Statistics, Persian Gulf University, Bushehr, Iran.
Gamze Ozel: Department of Statistics, Hacettepe University, Turkey.
Hüseyin Tatlidil: Department of Statistics, Hacettepe University, Turkey.
Najmieh Maksayi: Faculty of Mathematics, University of Sistan and Balouchestan, Iran.

Journal for Economic Forecasting, 2017, issue 4, 97-115

Abstract: The purpose of this study is to evaluate the forecasting ability of GARCH-type models in estimating the Value-at-Risk (VaR) by introducing a new four-parameter distribution, called Exponentiated Odd Log-Logistic Normal distribution. The statistical properties of new heavytailed distribution are investigated and a simulation study is performed to assess the maximum likelihood estimations of introduced distribution. Then, the VaR is forecasted by using mean and volatility forecasts and quantile estimation of introduced distribution. Daily VaR forecasting ability of proposed two-stage model is compared with the GARCH models specified under heavy-tailed distributions by means of two backtesting methods. Empirical findings show that proposed two-stage model outperforms to well-known distributions such as normal, Student’s-t, generalized error, and skewed generalized error distributions at high quantiles.

Keywords: Value-at-Risk; GARCH model; log–logistic distribution; maximum likelihood; estimation; normal distribution (search for similar items in EconPapers)
JEL-codes: C46 C53 G32 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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